New ideas to increase the safety and reliability of multicopters have become critical to optimizing maintenance activities, reducing fatalities in unmanned aircraft, increasing surveillance reliability in drones, and improving mission effectiveness in vertical take-off and landing (VTOL) aircraft.

RPI innovators created a Multicopter Online Rotor Fault Diagnosis System that can achieve fault detection, identification (classification), and fault magnitude estimation (quantification) – collectively referred to as fault diagnosis – in multirotor / VTOL aircraft via the use of distributed strain sensors placed on the aircraft booms close to the rotors and a data-driven machine-learning (ML) framework that combines neural networks (NN) and linear regression schemes. The result is an unprecedented capability to assess rotor failure in multicopter-based aircraft. Key Benefits of the creation include: lower false-positive rate in rotor-fault diagnostics, ease of integration into existing multicopter platforms, operational safety (reliability) improvement, and greater mission accomplishment rates.